Overview

Dataset statistics

Number of variables22
Number of observations3883
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory667.5 KiB
Average record size in memory176.0 B

Variable types

Numeric3
Categorical19

Alerts

Title has a high cardinality: 3883 distinct values High cardinality
df_index is highly correlated with MovieIDHigh correlation
MovieID is highly correlated with df_indexHigh correlation
Animation is highly correlated with Children'sHigh correlation
Children's is highly correlated with AnimationHigh correlation
df_index is uniformly distributed Uniform
MovieID is uniformly distributed Uniform
Title is uniformly distributed Uniform
df_index has unique values Unique
MovieID has unique values Unique
Title has unique values Unique

Reproduction

Analysis started2022-07-14 03:03:31.354740
Analysis finished2022-07-14 03:05:12.820504
Duration1 minute and 41.47 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct3883
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1941
Minimum0
Maximum3882
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size30.5 KiB
2022-07-13T23:05:12.879069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile194.1
Q1970.5
median1941
Q32911.5
95-th percentile3687.9
Maximum3882
Range3882
Interquartile range (IQR)1941

Descriptive statistics

Standard deviation1121.069876
Coefficient of variation (CV)0.5775733518
Kurtosis-1.2
Mean1941
Median Absolute Deviation (MAD)971
Skewness0
Sum7536903
Variance1256797.667
MonotonicityNot monotonic
2022-07-13T23:05:12.935243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38821
 
< 0.1%
36471
 
< 0.1%
26861
 
< 0.1%
20801
 
< 0.1%
33191
 
< 0.1%
20641
 
< 0.1%
13731
 
< 0.1%
36581
 
< 0.1%
13741
 
< 0.1%
33221
 
< 0.1%
Other values (3873)3873
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
38821
< 0.1%
38811
< 0.1%
38801
< 0.1%
38791
< 0.1%
38781
< 0.1%
38771
< 0.1%
38761
< 0.1%
38751
< 0.1%
38741
< 0.1%
38731
< 0.1%

MovieID
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct3883
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.049446
Minimum1
Maximum3952
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.5 KiB
2022-07-13T23:05:12.994331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile196.1
Q1982.5
median2010
Q32980.5
95-th percentile3756.9
Maximum3952
Range3951
Interquartile range (IQR)1998

Descriptive statistics

Standard deviation1146.778349
Coefficient of variation (CV)0.5774168169
Kurtosis-1.214552412
Mean1986.049446
Median Absolute Deviation (MAD)999
Skewness-0.01908154472
Sum7711830
Variance1315100.583
MonotonicityNot monotonic
2022-07-13T23:05:13.054556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39521
 
< 0.1%
37161
 
< 0.1%
27551
 
< 0.1%
21491
 
< 0.1%
33881
 
< 0.1%
21331
 
< 0.1%
13941
 
< 0.1%
37271
 
< 0.1%
13951
 
< 0.1%
33911
 
< 0.1%
Other values (3873)3873
99.7%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
39521
< 0.1%
39511
< 0.1%
39501
< 0.1%
39491
< 0.1%
39481
< 0.1%
39471
< 0.1%
39461
< 0.1%
39451
< 0.1%
39441
< 0.1%
39431
< 0.1%

Title
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct3883
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
Contender, The (2000)
 
1
Fatal Beauty (1987)
 
1
Light of Day (1987)
 
1
House II: The Second Story (1987)
 
1
Harry and the Hendersons (1987)
 
1
Other values (3878)
3878 

Length

Max length82
Median length68
Mean length24.20267834
Min length8

Characters and Unicode

Total characters93979
Distinct characters98
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3883 ?
Unique (%)100.0%

Sample

1st rowContender, The (2000)
2nd rowWhipped (2000)
3rd rowBig Momma's House (2000)
4th rowIsn't She Great? (2000)
5th rowShanghai Noon (2000)

Common Values

ValueCountFrequency (%)
Contender, The (2000)1
 
< 0.1%
Fatal Beauty (1987)1
 
< 0.1%
Light of Day (1987)1
 
< 0.1%
House II: The Second Story (1987)1
 
< 0.1%
Harry and the Hendersons (1987)1
 
< 0.1%
Adventures in Babysitting (1987)1
 
< 0.1%
Raising Arizona (1987)1
 
< 0.1%
Near Dark (1987)1
 
< 0.1%
Tin Men (1987)1
 
< 0.1%
Who's That Girl? (1987)1
 
< 0.1%
Other values (3873)3873
99.7%

Length

2022-07-13T23:05:13.121756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1251
 
8.0%
of364
 
2.3%
1996345
 
2.2%
1995342
 
2.2%
1998337
 
2.1%
1997315
 
2.0%
1999283
 
1.8%
1994257
 
1.6%
a170
 
1.1%
1993165
 
1.1%
Other values (4646)11846
75.6%

Most occurring characters

ValueCountFrequency (%)
11792
 
12.5%
e6698
 
7.1%
96463
 
6.9%
a4224
 
4.5%
)4153
 
4.4%
(4153
 
4.4%
13949
 
4.2%
o3882
 
4.1%
n3537
 
3.8%
r3421
 
3.6%
Other values (88)41707
44.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter46031
49.0%
Decimal Number15821
 
16.8%
Space Separator11792
 
12.5%
Uppercase Letter10193
 
10.8%
Close Punctuation4153
 
4.4%
Open Punctuation4153
 
4.4%
Other Punctuation1766
 
1.9%
Dash Punctuation68
 
0.1%
Currency Symbol1
 
< 0.1%
Other Number1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6698
14.6%
a4224
9.2%
o3882
 
8.4%
n3537
 
7.7%
r3421
 
7.4%
i3406
 
7.4%
t3176
 
6.9%
s2510
 
5.5%
h2392
 
5.2%
l2269
 
4.9%
Other values (32)10516
22.8%
Uppercase Letter
ValueCountFrequency (%)
T1391
13.6%
S868
 
8.5%
M730
 
7.2%
B702
 
6.9%
C616
 
6.0%
A587
 
5.8%
D522
 
5.1%
L497
 
4.9%
P470
 
4.6%
F466
 
4.6%
Other values (19)3344
32.8%
Other Punctuation
ValueCountFrequency (%)
,1025
58.0%
'224
 
12.7%
:198
 
11.2%
.193
 
10.9%
!44
 
2.5%
&41
 
2.3%
?17
 
1.0%
/16
 
0.9%
*6
 
0.3%
;1
 
0.1%
Decimal Number
ValueCountFrequency (%)
96463
40.9%
13949
25.0%
81113
 
7.0%
7743
 
4.7%
6727
 
4.6%
0718
 
4.5%
5670
 
4.2%
4540
 
3.4%
2490
 
3.1%
3408
 
2.6%
Space Separator
ValueCountFrequency (%)
11792
100.0%
Close Punctuation
ValueCountFrequency (%)
)4153
100.0%
Open Punctuation
ValueCountFrequency (%)
(4153
100.0%
Dash Punctuation
ValueCountFrequency (%)
-68
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%
Other Number
ValueCountFrequency (%)
³1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin56224
59.8%
Common37755
40.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6698
 
11.9%
a4224
 
7.5%
o3882
 
6.9%
n3537
 
6.3%
r3421
 
6.1%
i3406
 
6.1%
t3176
 
5.6%
s2510
 
4.5%
h2392
 
4.3%
l2269
 
4.0%
Other values (61)20709
36.8%
Common
ValueCountFrequency (%)
11792
31.2%
96463
17.1%
)4153
 
11.0%
(4153
 
11.0%
13949
 
10.5%
81113
 
2.9%
,1025
 
2.7%
7743
 
2.0%
6727
 
1.9%
0718
 
1.9%
Other values (17)2919
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII93917
99.9%
None62
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11792
 
12.6%
e6698
 
7.1%
96463
 
6.9%
a4224
 
4.5%
)4153
 
4.4%
(4153
 
4.4%
13949
 
4.2%
o3882
 
4.1%
n3537
 
3.8%
r3421
 
3.6%
Other values (68)41645
44.3%
None
ValueCountFrequency (%)
é25
40.3%
è6
 
9.7%
ö4
 
6.5%
à4
 
6.5%
í3
 
4.8%
ø2
 
3.2%
É2
 
3.2%
î2
 
3.2%
á2
 
3.2%
ó2
 
3.2%
Other values (10)10
 
16.1%

year
Real number (ℝ≥0)

Distinct81
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.066959
Minimum1919
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.5 KiB
2022-07-13T23:05:13.177987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1919
5-th percentile1946
Q11982
median1994
Q31997
95-th percentile1999
Maximum2000
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation16.89569016
Coefficient of variation (CV)0.008507110037
Kurtosis2.402797142
Mean1986.066959
Median Absolute Deviation (MAD)4
Skewness-1.766093575
Sum7711898
Variance285.4643459
MonotonicityDecreasing
2022-07-13T23:05:13.240004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1996345
 
8.9%
1995342
 
8.8%
1998337
 
8.7%
1997315
 
8.1%
1999283
 
7.3%
1994257
 
6.6%
1993165
 
4.2%
2000156
 
4.0%
1986104
 
2.7%
1992102
 
2.6%
Other values (71)1477
38.0%
ValueCountFrequency (%)
19193
 
0.1%
19202
 
0.1%
19211
 
< 0.1%
19222
 
0.1%
19233
 
0.1%
19256
0.2%
19268
0.2%
19276
0.2%
19283
 
0.1%
19293
 
0.1%
ValueCountFrequency (%)
2000156
4.0%
1999283
7.3%
1998337
8.7%
1997315
8.1%
1996345
8.9%
1995342
8.8%
1994257
6.6%
1993165
4.2%
1992102
 
2.6%
199160
 
1.5%

Action
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3380 
1
503 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03380
87.0%
1503
 
13.0%

Length

2022-07-13T23:05:13.297508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:13.342940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03380
87.0%
1503
 
13.0%

Most occurring characters

ValueCountFrequency (%)
03380
87.0%
1503
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03380
87.0%
1503
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03380
87.0%
1503
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03380
87.0%
1503
 
13.0%

Adventure
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3600 
1
 
283

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03600
92.7%
1283
 
7.3%

Length

2022-07-13T23:05:13.384168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:13.423139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03600
92.7%
1283
 
7.3%

Most occurring characters

ValueCountFrequency (%)
03600
92.7%
1283
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03600
92.7%
1283
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03600
92.7%
1283
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03600
92.7%
1283
 
7.3%

Animation
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3778 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03778
97.3%
1105
 
2.7%

Length

2022-07-13T23:05:13.467493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:13.505377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03778
97.3%
1105
 
2.7%

Most occurring characters

ValueCountFrequency (%)
03778
97.3%
1105
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03778
97.3%
1105
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03778
97.3%
1105
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03778
97.3%
1105
 
2.7%

Children's
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3632 
1
 
251

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03632
93.5%
1251
 
6.5%

Length

2022-07-13T23:05:13.543370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:13.578728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03632
93.5%
1251
 
6.5%

Most occurring characters

ValueCountFrequency (%)
03632
93.5%
1251
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03632
93.5%
1251
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03632
93.5%
1251
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03632
93.5%
1251
 
6.5%

Comedy
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
2683 
1
1200 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
02683
69.1%
11200
30.9%

Length

2022-07-13T23:05:13.613499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:13.649577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
02683
69.1%
11200
30.9%

Most occurring characters

ValueCountFrequency (%)
02683
69.1%
11200
30.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02683
69.1%
11200
30.9%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02683
69.1%
11200
30.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02683
69.1%
11200
30.9%

Crime
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3672 
1
 
211

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03672
94.6%
1211
 
5.4%

Length

2022-07-13T23:05:13.681276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:13.719223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03672
94.6%
1211
 
5.4%

Most occurring characters

ValueCountFrequency (%)
03672
94.6%
1211
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03672
94.6%
1211
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03672
94.6%
1211
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03672
94.6%
1211
 
5.4%

Documentary
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3756 
1
 
127

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03756
96.7%
1127
 
3.3%

Length

2022-07-13T23:05:13.753438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:13.793814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03756
96.7%
1127
 
3.3%

Most occurring characters

ValueCountFrequency (%)
03756
96.7%
1127
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03756
96.7%
1127
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03756
96.7%
1127
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03756
96.7%
1127
 
3.3%

Drama
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
2280 
1
1603 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02280
58.7%
11603
41.3%

Length

2022-07-13T23:05:13.826610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:13.871245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
02280
58.7%
11603
41.3%

Most occurring characters

ValueCountFrequency (%)
02280
58.7%
11603
41.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02280
58.7%
11603
41.3%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02280
58.7%
11603
41.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02280
58.7%
11603
41.3%

Fantasy
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3815 
1
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Length

2022-07-13T23:05:13.906184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:13.943652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Most occurring characters

ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Film-Noir
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3839 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03839
98.9%
144
 
1.1%

Length

2022-07-13T23:05:13.977168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:14.014986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03839
98.9%
144
 
1.1%

Most occurring characters

ValueCountFrequency (%)
03839
98.9%
144
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03839
98.9%
144
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03839
98.9%
144
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03839
98.9%
144
 
1.1%

Horror
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3540 
1
 
343

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03540
91.2%
1343
 
8.8%

Length

2022-07-13T23:05:14.054758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:14.094780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03540
91.2%
1343
 
8.8%

Most occurring characters

ValueCountFrequency (%)
03540
91.2%
1343
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03540
91.2%
1343
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03540
91.2%
1343
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03540
91.2%
1343
 
8.8%

Musical
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3769 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03769
97.1%
1114
 
2.9%

Length

2022-07-13T23:05:14.142080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:14.181857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03769
97.1%
1114
 
2.9%

Most occurring characters

ValueCountFrequency (%)
03769
97.1%
1114
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03769
97.1%
1114
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03769
97.1%
1114
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03769
97.1%
1114
 
2.9%

Mystery
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3777 
1
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03777
97.3%
1106
 
2.7%

Length

2022-07-13T23:05:14.224360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:14.262361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03777
97.3%
1106
 
2.7%

Most occurring characters

ValueCountFrequency (%)
03777
97.3%
1106
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03777
97.3%
1106
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03777
97.3%
1106
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03777
97.3%
1106
 
2.7%

Romance
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3412 
1
471 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03412
87.9%
1471
 
12.1%

Length

2022-07-13T23:05:14.305658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:14.342332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03412
87.9%
1471
 
12.1%

Most occurring characters

ValueCountFrequency (%)
03412
87.9%
1471
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03412
87.9%
1471
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03412
87.9%
1471
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03412
87.9%
1471
 
12.1%

Sci-Fi
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3607 
1
 
276

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03607
92.9%
1276
 
7.1%

Length

2022-07-13T23:05:14.376000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:14.414674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03607
92.9%
1276
 
7.1%

Most occurring characters

ValueCountFrequency (%)
03607
92.9%
1276
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03607
92.9%
1276
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03607
92.9%
1276
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03607
92.9%
1276
 
7.1%

Thriller
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3391 
1
492 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03391
87.3%
1492
 
12.7%

Length

2022-07-13T23:05:14.447184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:14.484180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03391
87.3%
1492
 
12.7%

Most occurring characters

ValueCountFrequency (%)
03391
87.3%
1492
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03391
87.3%
1492
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03391
87.3%
1492
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03391
87.3%
1492
 
12.7%

War
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3740 
1
 
143

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03740
96.3%
1143
 
3.7%

Length

2022-07-13T23:05:14.519331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:14.555528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03740
96.3%
1143
 
3.7%

Most occurring characters

ValueCountFrequency (%)
03740
96.3%
1143
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03740
96.3%
1143
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03740
96.3%
1143
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03740
96.3%
1143
 
3.7%

Western
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.5 KiB
0
3815 
1
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3883
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Length

2022-07-13T23:05:14.586326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-13T23:05:14.623390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Most occurring characters

ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3883
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common3883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03815
98.2%
168
 
1.8%

Interactions

2022-07-13T23:04:58.909330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-13T23:03:33.990220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-13T23:03:47.511421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-13T23:04:58.975732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-13T23:03:34.159368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-13T23:03:52.994350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-13T23:05:11.734465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-13T23:03:47.408736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-13T23:04:10.993101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-13T23:05:14.664062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-13T23:05:14.752342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-13T23:05:14.844164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-13T23:05:14.927085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-13T23:05:15.015520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-13T23:05:11.948806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-13T23:05:12.548406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexMovieIDTitleyearActionAdventureAnimationChildren'sComedyCrimeDocumentaryDramaFantasyFilm-NoirHorrorMusicalMysteryRomanceSci-FiThrillerWarWestern
038823952Contender, The (2000)2000000000010000000100
135283597Whipped (2000)2000000010000000000000
235773646Big Momma's House (2000)2000000010000000000000
331703239Isn't She Great? (2000)2000000010000000000000
435553624Shanghai Noon (2000)2000100000000000000000
535543623Mission: Impossible 2 (2000)2000100000000000000100
635493618Small Time Crooks (2000)2000000010000000000000
735483617Road Trip (2000)2000000010000000000000
835473616Loser (2000)2000000010000000010000
935463615Dinosaur (2000)2000001100000000000000

Last rows

df_indexMovieIDTitleyearActionAdventureAnimationChildren'sComedyCrimeDocumentaryDramaFantasyFilm-NoirHorrorMusicalMysteryRomanceSci-FiThrillerWarWestern
387335723641Woman of Paris, A (1923)1923000000010000000000
387421612230Always Tell Your Wife (1923)1923000010000000000000
387531263195Tess of the Storm Country (1922)1922000000010000000000
387613271348Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)1922000000000010000000
387732413310Kid, The (1921)1921100000000000000000
387832403309Dog's Life, A (1920)1920000010000000000000
387931623231Saphead, The (1920)1920000010000000000000
388027542823Spiders, The (Die Spinnen, 1. Teil: Der Goldene See) (1919)1919100000010000000000
388130633132Daddy Long Legs (1919)1919000010000000000000
388227522821Male and Female (1919)1919010000010000000000